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Natural Language Processing and Computational Linguistics

You're reading from Natural Language Processing and Computational Linguistics A practical guide to text analysis with Python, Gensim, spaCy, and Keras

Product type Paperback

Published in Jun 2018

Publisher Packt

ISBN-13 9781788838535

Length 306 pages

Edition 1st Edition

Languages

Processing

Tools

Keras

Concepts

Mobile Application Development

Author (1):

Bhargav Srinivasa-Desikan

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Table of Contents (17) Chapters

Preface

1. What is Text Analysis?

2. Python Tips for Text Analysis FREE CHAPTER

3. spaCy's Language Models

4. Gensim – Vectorizing Text and Transformations and n-grams

5. POS-Tagging and Its Applications

6. NER-Tagging and Its Applications

7. Dependency Parsing

8. Topic Models

9. Advanced Topic Modeling

10. Clustering and Classifying Text

11. Similarity Queries and Summarization

12. Word2Vec, Doc2Vec, and Gensim

13. Deep Learning for Text

14. Keras and spaCy for Deep Learning

15. Sentiment Analysis and ChatBots

16. Other Books You May Enjoy

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References

[1] Introduction to Structural Syntax:
http://www.home.uni-osnabrueck.de/bschwisc/archives/tesniere.pdf

[2] Term Logic:
https://plato.stanford.edu/archives/win2016/entries/logic-ancient/

[3] Noam Chomsky:
https://en.wikipedia.org/wiki/Noam_Chomsky

[4] Image link:
https://en.wikipedia.org/wiki/Phrase_structure_grammar#/media/File:Thistreeisillustratingtherelation(PSG).png

[5] Phrase Structure Grammar:
https://en.wikipedia.org/wiki/Phrase_structure_grammar

[6] Dependency Grammar and Dependency Parsing:
http://stp.lingfil.uu.se/~nivre/docs/05133.pdf

[7] Dependency Grammar:
https://en.wikipedia.org/wiki/Dependency_grammar

[8] CLEAR style:
http://www.mathcs.emory.edu/~choi/doc/cu-2012-choi.pdf

[9] displaCy:
https://explosion.ai/demos/displacy

[10] spaCy annotation page:
https://spacy.io/api/annotation#dependency-parsing

[11] Stanford Dependency Parser:
https:/...

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Authors (1)

Bhargav Srinivasa-Desikan

Bhargav Srinivasa-Desikan

Bhargav Srinivasa-Desikan is a research engineer working for INRIA in Lille, France. He is a part of the MODAL (Models of Data Analysis and Learning) team, and he works on metric learning, predictor aggregation, and data visualization. He is a regular contributor to the Python open source community, and completed Google Summer of Code in 2016 with Gensim where he implemented Dynamic Topic Models. He is a regular speaker at PyCons and PyDatas across Europe and Asia, and conducts tutorials on text analysis using Python.

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